A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk
A growing number of people are working as part of on-line crowd work, which has been characterized by its low wages; yet, we know little about wage distribution and causes of low/high earnings. We recorded 2,676 workers performing 3.8 million tasks on Amazon Mechanical Turk. Our task-level analysis...
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| creator | Hara, Kotaro Adams, Abi Milland, Kristy Savage, Saiph Callison-Burch, Chris Bigham, Jeffrey |
| description | A growing number of people are working as part of on-line crowd work, which
has been characterized by its low wages; yet, we know little about wage
distribution and causes of low/high earnings. We recorded 2,676 workers
performing 3.8 million tasks on Amazon Mechanical Turk. Our task-level analysis
revealed that workers earned a median hourly wage of only ~\$2/h, and only 4%
earned more than \$7.25/h. The average requester pays more than \$11/h,
although lower-paying requesters post much more work. Our wage calculations are
influenced by how unpaid work is included in our wage calculations, e.g., time
spent searching for tasks, working on tasks that are rejected, and working on
tasks that are ultimately not submitted. We further explore the characteristics
of tasks and working patterns that yield higher hourly wages. Our analysis
informs future platform design and worker tools to create a more positive
future for crowd work. |
| doi_str_mv | 10.48550/arxiv.1712.05796 |
| format | Article |
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has been characterized by its low wages; yet, we know little about wage
distribution and causes of low/high earnings. We recorded 2,676 workers
performing 3.8 million tasks on Amazon Mechanical Turk. Our task-level analysis
revealed that workers earned a median hourly wage of only ~\$2/h, and only 4%
earned more than \$7.25/h. The average requester pays more than \$11/h,
although lower-paying requesters post much more work. Our wage calculations are
influenced by how unpaid work is included in our wage calculations, e.g., time
spent searching for tasks, working on tasks that are rejected, and working on
tasks that are ultimately not submitted. We further explore the characteristics
of tasks and working patterns that yield higher hourly wages. Our analysis
informs future platform design and worker tools to create a more positive
future for crowd work.</description><identifier>DOI: 10.48550/arxiv.1712.05796</identifier><language>eng</language><subject>Computer Science - Computers and Society ; Computer Science - Human-Computer Interaction</subject><creationdate>2017-12</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,781,886</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1712.05796$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1712.05796$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Hara, Kotaro</creatorcontrib><creatorcontrib>Adams, Abi</creatorcontrib><creatorcontrib>Milland, Kristy</creatorcontrib><creatorcontrib>Savage, Saiph</creatorcontrib><creatorcontrib>Callison-Burch, Chris</creatorcontrib><creatorcontrib>Bigham, Jeffrey</creatorcontrib><title>A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk</title><description>A growing number of people are working as part of on-line crowd work, which
has been characterized by its low wages; yet, we know little about wage
distribution and causes of low/high earnings. We recorded 2,676 workers
performing 3.8 million tasks on Amazon Mechanical Turk. Our task-level analysis
revealed that workers earned a median hourly wage of only ~\$2/h, and only 4%
earned more than \$7.25/h. The average requester pays more than \$11/h,
although lower-paying requesters post much more work. Our wage calculations are
influenced by how unpaid work is included in our wage calculations, e.g., time
spent searching for tasks, working on tasks that are rejected, and working on
tasks that are ultimately not submitted. We further explore the characteristics
of tasks and working patterns that yield higher hourly wages. Our analysis
informs future platform design and worker tools to create a more positive
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has been characterized by its low wages; yet, we know little about wage
distribution and causes of low/high earnings. We recorded 2,676 workers
performing 3.8 million tasks on Amazon Mechanical Turk. Our task-level analysis
revealed that workers earned a median hourly wage of only ~\$2/h, and only 4%
earned more than \$7.25/h. The average requester pays more than \$11/h,
although lower-paying requesters post much more work. Our wage calculations are
influenced by how unpaid work is included in our wage calculations, e.g., time
spent searching for tasks, working on tasks that are rejected, and working on
tasks that are ultimately not submitted. We further explore the characteristics
of tasks and working patterns that yield higher hourly wages. Our analysis
informs future platform design and worker tools to create a more positive
future for crowd work.</abstract><doi>10.48550/arxiv.1712.05796</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computers and Society Computer Science - Human-Computer Interaction |
| title | A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk |
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